Publications

2025

1. Arxiv

   Efficient Unified Caching for Accelerating Heterogeneous AI Workloads

   Tianze Wang, Yifei Liu, Chen Chen, Pengfei Zuo, Jiawei Zhang, Qizhen Weng, Yin Chen, Zhenhua Han, Jieru Zhao, Quan Chen, and  others

   arXiv preprint arXiv:2506.12370, 2025

   PDF
2. Arxiv

   Efficient Serving of LLM Applications with Probabilistic Demand Modeling

   Yifei Liu, Zuo Gan, Zhenghao Gan, Weiye Wang, Chen Chen, Yizhou Shan, Xusheng Chen, Zhenhua Han, Yifei Zhu, Shixuan Sun, and  others

   arXiv preprint arXiv:2506.14851, 2025

   PDF
3. TACO

   EDAS: Enabling Fast Data Loading for GPU Serverless Computing

   Han Zhao, Weihao Cui, Quan Chen, Zijun Li, Zhenhua Han, Nan Wang, Yu Feng, Jieru Zhao, Chen Chen, Jingwen Leng, and  others

   ACM Transactions on Architecture and Code Optimization, 2025
4. ICDCS

   LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications

   Botao Zhu, Chen Chen, Xiaoyi Fan, and Yifei Zhu

   In IEEE International Conference on Distributed Computing Systems, 2025
5. ICDCS

   FedSU: Communication-efficient Federated Learning with Speculative Updating

   Wei Yu, Chen Chen, Qinbin Li, Jieru Zhao, Shixuan Sun, Bo Li, and Minyi Guo

   In IEEE International Conference on Distributed Computing Systems, 2025
6. ISCA

   Lumina: Real-Time Neural Rendering by Exploiting Computational Redundancy

   Yu Feng, Weikai Lin, Yuge Cheng, Zihan Liu, Jingwen Leng, Minyi Guo, Chen Chen, Shixuan Sun, and Yuhao Zhu

   In International Symposium on Computer Architecture, 2025
7. TACO

   Taming Flexible Job Packing in Deep Learning Training Clusters

   Pengyu Yang, Weihao Cui, Chunyu Xue, Han Zhao, Chen Chen, Quan Chen, Jing Yang, and Minyi Guo

   ACM Transactions on Architecture and Code Optimization, 2025

   PDF
8. IPDPS

   Reducing the End-to-End Latency of DNN-based Recommendation Systems Deployed in GPU Pools

   Guangqiang Luan, Pu Pang, Quan Chen, Guoyao Xu, Chi Zhang, Yanyi Zi, Yinghao Yu, Guodong Yang, Liping Zhang, Chen Chen, and Minyi Guo

   In 39th IEEE International Parallel and Distributed Processing Symposium, 2025

   PDF

2024

1. ENLSP

   RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval

   Di Liu, Meng Chen, Baotong Lu, Huiqiang Jiang, Zhenhua Han, Qianxi Zhang, Qi Chen, Chengruidong Zhang, Bailu Ding, Kai Zhang, Chen Chen, Fan Yang, Yuqing Yang, and Lili Qiu

   In the 4th NeurIPS Workshop on Efficient Natural Language and Speech Processing (Spotlight), 2024

   Best Paper Award PDF

   Awarded the best paper by ENLSP

2. ICPP

   FedCA: Efficient Federated Learning with Client Autonomy

   Na Lv, Zhi Shen, Chen Chen, Zhifeng Jiang, Jiayi Zhang, Quan Chen, and Minyi Guo

   In Proceedings of the 53rd ACM International Conference on Parallel Processing, 2024

   PDF
3. IWQoS

   PAS: Towards Accurate and Efficient Federated Learning with Parameter-Adaptive Synchronization

   Zuo Gan, Chen Chen, Jiayi Zhang, Gaoxiong Zeng, Yifei Zhu, Jieru Zhao, Quan Chen, and Minyi Guo

   In Proceedings of the IEEE/ACM International Symposium on Quality of Service, 2024

   PDF
4. IWQoS Poster

   Towards Efficient Compound Large Language Model System Serving in the Wild

   Yifei Zhu, Botao Zhu, Chen Chen, and Xiaoyi Fan

   In Proceedings of the IEEE/ACM International Symposium on Quality of Service, 2024

   Best Poster Award PDF

   The best poster for IWQoS 2024

5. OSDI

   Parrot: Efficient Serving of LLM-based Applications with Semantic Variable

   Chaofan Lin, Zhenhua Han, Chengruidong Zhang, Yuqing Yang, Fan Yang, Chen Chen, and Lili Qiu

   In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, 2024

   Abs PDF

   The rise of large language models (LLMs) has enabled LLM-based applications (a.k.a. AI agents or co-pilots), a new software paradigm that combines the strength of LLM and conventional software. Diverse LLM applications from different tenants could design complex workflows using multiple LLM requests to accomplish one task. However, they have to use the over-simplified request-level API provided by today’s public LLM services, losing essential application-level information. Public LLM services have to blindly optimize individual LLM requests, leading to sub-optimal end-to-end performance of LLM applications.

6. INFOCOM

   DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service

   Yu Liu, Zibo Wang, Yifei Zhu, and Chen Chen

   In Proceedings of the IEEE Conference on Computer Communications, 2024

   PDF
7. HPCA

   An Optimizing Framework on MLIR for Efficient FPGA-based Accelerator Generation

   Weichuang Zhang, Jieru Zhao, Guan Shen, Quan Chen, Chen Chen, and Minyi Guo

   In Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2024

   Distinguished Artifact Award Runner-up PDF

   The paper with the best artifact in HPCA 2024 (2 out of 75 accepted papers and 410 submissions)

8. ASPLOS

   DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration

   Zijun Li, Chuhao Xu, Quan Chen, Jieru Zhao, Chen Chen, and Minyi Guo

   In Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

   PDF Video

2023

1. ICCD

   STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs

   Jiawen Wang, Quan Chen, Deze Zeng, Zhuo Song, Chen Chen, and Minyi Guo

   In Proceedings of the IEEE International Conference on Computer Design, 2023

   PDF
2. TCC

   Accelerating Distributed Learning in Non-Dedicated Environments

   Chen Chen, Qizhen Weng, Wei Wang, Baochun Li, and Bo Li

   IEEE Transactions on Cloud Computing, 2023

   Abs PDF

   Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by stragglers—workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called semi-dynamic load balancing, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers’ load to their instantaneous processing capabilities—by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to 54%.

3. ICPP

   Asfl: Adaptive Semi-asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks

   Jieling Yu, Ruiting Zhou, Chen Chen, Bo Li, and Fang Dong

   In Proceedings of the ACM International Conference on Parallel Processing, 2023

   PDF
4. TPDS

   Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively

   Chen Chen, Hong Xu, Wei Wang, Baochun Li, Bo Li, Li Chen, and Gong Zhang

   IEEE Transactions on Parallel and Distributed Systems, 2023

   Abs PDF

   Federated learning allows edge devices to collaboratively train a global model without sharing their local private data. Yet, with limited network bandwidth at the edge, communication often becomes a severe bottleneck. In this paper, we find that it is unnecessary to always synchronize the full model in the entire training process, because many parameters already become mature (i.e., stable) prior to model convergence, and can thus be excluded from later synchronizations. This allows us to reduce the communication overhead without compromising the model accuracy. However, challenges are that the local parameters excluded from global synchronization may diverge on different clients, and meanwhile some parameters may stabilize only temporally. To address these challenges, we propose a novel scheme called Adaptive Parameter Freezing (APF), which fixes (freezes) the non-synchronized stable parameters in intermittent periods. Specifically, the freezing periods are tentatively adjusted in an additively-increase and multiplicativelydecrease manner—depending on whether the previously-frozen parameters remain stable in subsequent iterations. We also extend APF into APF# and APF++, which freeze parameters in a more aggressive manner to achieve larger performance benefit for large complex models. We implemented APF and its variants as Python modules with PyTorch, and extensive experiments show that APF can reduce data transfer amount by over 60%.

5. JSAC

   GIFT: Towards Accurate and Efficient Federated Learning with Gradient-Instructed Frequency Tuning

   Chen Chen, Hong Xu, Baochun Li, Bo Li, Li Chen, and Gong Zhang

   IEEE Journal on Selected Areas in Communications (special issue on Communication-Efficient Distributed Learning over Networks), 2023

   Abs PDF

   Federated Learning (FL) enables distributed clients to collectively train a global model without revealing their private data, and for efficiency clients synchronize their gradients periodically. However, this can lead to the inaccuracy in model convergence due to inconsistent data distributions among clients. In this work, we find that there is a strong correlation between FL accuracy loss and the synchronization frequency, and seek to fine tune the synchronization frequency at training runtime to make FL accurate and also efficient. Specifically, aware that under the FL privacy requirement only gradients can be utilized for making frequency tuning decisions, we propose a novel metric called gradient consistency, which can effectively reflect the training status despite the instability of realistic FL scenarios. We further devise a feedback-driven algorithm called GradientInstructed Frequency Tuning (GIFT), which adaptively increases or decreases the synchronization frequency based on the gradient consistency metric. We have implemented GIFT in PyTorch, and large-scale evaluations show that it can improve FL accuracy by up to 10.7% with a time reduction of 58.1%.

2022

1. SoCC

   Characterizing and orchestrating VM reservation in geo-distributed clouds to improve the resource efficiency

   Jiuchen Shi, Kaihua Fu, Quan Chen, Changpeng Yang, Pengfei Huang, Mosong Zhou, Jieru Zhao, Chen Chen, and Minyi Guo

   In Proceedings of the ACM Symposium on Cloud Computing, 2022

   Abs PDF

   Cloud providers often build a geo-distributed cloud from multiple datacenters in different geographic regions, to serve tenants at different locations. The tenants that run large scale applications often reserve resources based on their peak loads in the region close to the end users to handle the ever changing application load, wasting a large amount of resources. We therefore characterize the VM request patterns of the top tenants in our production public geo-distributed cloud, and open-source the VM request traces in four months from the top 20 tenants of our cloud. The characterization shows that the resource usage of large tenants has various temporal and spatial patterns on the dimensions of time series, regions, and VM types, and has the potential of peak shaving between different tenants to further reduce the resource reservation cost. Based on the findings, we propose a resource reservation and VM request scheduling scheme named ROS to minimize the resource reservation cost while satisfying the VM allocation requests. Our experiments show that ROS reduces the overall deployment cost by 75.4% and the reservation resources by 60.1%, compared to the tenant-specified reservation strategy.

2021

1. ICDCS

   Communication-Efficient Federated Learning with Adaptive Parameter Freezing

   Chen Chen, Hong Xu, Wei Wang, Baochun Li, Bo Li, Li Chen, and Gong Zhang

   In Proceedings of the IEEE International Conference on Distributed Computing Systems, 2021

   Abs PDF Video

   Federated learning allows edge devices to collaboratively train a global model by synchronizing their local updates without sharing private data. Yet, with limited network bandwidth at the edge, communication often becomes a severe bottleneck. In this paper, we find that it is unnecessary to always synchronize the full model in the entire training process, because many parameters gradually stabilize prior to the ultimate model convergence, and can thus be excluded from being synchronized at an early stage. This allows us to reduce the communication overhead without compromising the model accuracy. However, challenges are that the local parameters excluded from global synchronization may diverge on different clients, and meanwhile some parameters may stabilize only temporally. To address these challenges, we propose a novel scheme called Adaptive Parameter Freezing (APF), which fixes (freezes) the non-synchronized stable parameters in intermittent periods. Specifically, the freezing periods are tentatively adjusted in an additively-increase and multiplicatively-decrease manner, depending on if the previouslyfrozen parameters remain stable in subsequent iterations. We implemented APF as a Python module in PyTorch. Our extensive array of experimental results show that APF can reduce data transfer by over 60%.

2. IJCNN

   Two-dimensional learning rate decay: Towards accurate federated learning with non-iid data

   Kaiwei Mo, Chen Chen, Jiamin Li, Hong Xu, and Chun Jason Xue

   In Proceedings of the International Joint Conference on Neural Networks, 2021

   PDF

2020

1. SoCC

   Semi-Dynamic Load Balancing: Efficient Distributed Learning in Non-Dedicated Environments

   Chen Chen, Qizhen Weng, Wei Wang, Baochun Li, and Bo Li

   In Proceedings of the ACM Symposium on Cloud Computing, 2020

   Abs PDF Video

   Machine learning (ML) models are increasingly trained in clusters with non-dedicated workers possessing heterogeneous resources. In such scenarios, model training efficiency can be negatively affected by stragglers—workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy called semi-dynamic load balancing to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being nonintrusive to intra-iteration execution. We develop LB-BSP based on such an insight, which is an integrated worker coordination mechanism that adapts workers’ load to their instantaneous processing capabilities by right-sizing the sample batches at the synchronization barriers. We have customdesigned the batch sizing algorithm respectively for CPU and GPU clusters based on their own characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to 54%.

2. SC

   Metis: Learning to schedule long-running applications in shared container clusters at scale

   Luping Wang, Qizhen Weng, Wei Wang, Chen Chen, and Bo Li

   In Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, 2020

   PDF

2019

1. INFOCOM

   Round-robin synchronization: Mitigating communication bottlenecks in parameter servers

   Chen Chen, Wei Wang, and Bo Li

   In Proceedings of the IEEE Conference on Computer Communications, 2019

   Best In-Session Presentation Award PDF

   The best presentation for the session CLOUD COMPUTING 2

2018

1. SoCC Poster

   Fast distributed deep learning via worker-adaptive batch sizing

   Chen Chen, Qizhen Weng, Wei Wang, Baochun Li, and Bo Li

   In Proceedings of the ACM symposium on cloud computing, 2018

   PDF
2. INFOCOM

   Performance-Aware Fair Scheduling: Exploiting Demand Elasticity of Data Analytics Jobs

   Chen Chen, Wei Wang, and Bo Li

   In Proceedings of the IEEE Conference on Computer Communications, 2018

   Abs PDF

   Efficient resource management is of paramount importance in today’s production clusters. In this paper, we identify the demand elasticity of data-parallel jobs. Demand elasticity allows jobs to run with a significantly less amount of resources than they ideally need, at the expense of only a modest performance penalty. Our EC2 experiment using popular Spark benchmark suites confirms that running a job using 50% of demanded slots is sufficient to achieve at least 75% of the ideal performance. We show that such an elasticity is an intrinsic property of data-parallel jobs and can be exploited to speed up average job completion. In this regard, we propose PerformanceAware Fair (PAF) scheduler to identify the demand elasticity and use it to improve the average job performance, while still attaining near-optimal isolation guarantee close to fair sharing. PAF starts with a fair allocation and iteratively adjusts it by transferring resources from one job to another, improving the performance of resource-taker without penalizing resource-giver by a noticeable amount. We implemented PAF in Spark and evaluated its effectiveness through both EC2 experiments and large-scale simulations. Evaluation results show that compared with fair allocation, PAF improves the average job performance by 13%, while penalizing resource-givers by no more than 1%.

2017

1. ICDCS

   Speculative Slot Reservation: Enforcing Service Isolation for Dependent Data-Parallel Computations

   Chen Chen, Wei Wang, and Bo Li

   In Proceedings of the IEEE International Conference on Distributed Computing Systems, 2017

   Abs PDF

   Priority scheduling is a fundamental tool to provide service isolation for different jobs in shared clusters. Ideally, the performance of a high-priority job should not be dragged down by another with a lower priority. However, we show in this paper that simply assigning a high priority provides no isolation for jobs with dependent computations. A job, even receiving the highest priority, may give up compute slots to another before proceeding to the downstream computation, which is because of barrier, i.e., that the downstream computation cannot start until all the upstream tasks have completed. Such an interruption of execution inevitably results in a significant delay.

2. INFOCOM

   Cluster fair queueing: Speeding up data-parallel jobs with delay guarantees

   Chen Chen, Wei Wang, Shengkai Zhang, and Bo Li

   In Proceedings of the IEEE Conference on Computer Communications, 2017

   Best In-Session Presentation Award PDF

   The best presentation for the session CLOUD COMPUTING 1

2016

1. ICC

   Software-defined inter-domain routing revisited

   Chen Chen, Bo Li, Dong Lin, and Baochun Li

   In Proceedings of the IEEE International Conference on Communications, 2016

   Abs PDF

   With software-defined networking (SDN), the control plane is fully decoupled from the data plane, which has been shown to improve routing performance and reduce route convergence time in the context of intra-domain routing. The applicability of software-defined networking to inter-domain routing, however, has not been fully explored. In this work, we first propose a mathematical model that attempts to quantify the BGP convergence time in an inter-domain routing environment, by simplifying the complex BGP convergence process. Based on our model and some practical observations, we first investigate how software-defined networking may help speed up interdomain routing, and then present a greedy algorithm that selects Autonomous Systems (ASes) for incremental SDN deployment to minimize the BGP convergence time. Our simulation results based on a real-world Internet topology have demonstrated the effectiveness of our proposed algorithm.